1 Ocak 2012 Dergi makalesi Açık Erişim

Izgi, Ercan; Oztopal, Ahmet; Yerli, Bihter; Kaymak, Mustafa Kemal; Sahin, Ahmet Duran

Dublin Core

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  <dc:creator>Izgi, Ercan</dc:creator>
  <dc:creator>Oztopal, Ahmet</dc:creator>
  <dc:creator>Yerli, Bihter</dc:creator>
  <dc:creator>Kaymak, Mustafa Kemal</dc:creator>
  <dc:creator>Sahin, Ahmet Duran</dc:creator>
  <dc:date>2012-01-01</dc:date>
  <dc:description>Solar irradiation is one of the major renewable energy sources and technologies related with this source have reached to high level applications. Prediction of solar irradiation shows some uncertainties depending on atmospheric parameters such as temperature, cloud amount, dust and relative humidity. These conditions add new uncertainties to the prediction of this astronomical parameter. In this case, prediction of generated electricity by photovoltaic or other solar technologies could be better than directly solar irradiation.</dc:description>
  <dc:identifier>https://aperta.ulakbim.gov.trrecord/83409</dc:identifier>
  <dc:identifier>oai:zenodo.org:83409</dc:identifier>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>http://www.opendefinition.org/licenses/cc-by</dc:rights>
  <dc:source>SOLAR ENERGY 86(2) 725-733</dc:source>
  <dc:title>Short-mid-term solar power prediction by using artificial neural networks</dc:title>
  <dc:type>info:eu-repo/semantics/article</dc:type>
  <dc:type>publication-article</dc:type>
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